English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Binary convolutional networks have lower computational load and lower memoryfoot-print compared to their full-precision counterparts. So, they are afeasible alternative for the deployment of computer vision applications onlimited capacity embedded devices. Once trained on less resource-constrainedcomputational environments, they can be deployed for real-time inference onsuch devices. In this study, we propose an implementation of binaryconvolutional network inference on GPU by focusing on optimization of XNORconvolution. Experimental results show that using GPU can provide a speed-up ofup to $42.61\times$ with a kernel size of $3\times3$. The implementation ispublicly available athttps://github.com/metcan/Binary-Convolutional-Neural-Network-Inference-on-GPU

Optimization of XNOR Convolution for Binary Convolutional Neural Networks on GPU